Risk field modeling of urban tunnel based on APF

References

• Babu F, Wang M, Arem BV, Happee R. 2020. Probabilistic field approach for motorway driving risk assessment. Transp Res Part C Emerg Technol. 118:102716. doi:10.1016/j.trc.2020.102716.
   Web of Science ®Google Scholar
• Byrne S, Naeem W, Ferguson S. 2015. Improved apf strategies for dual-arm local motion planning. Trans Inst Meas Control. 37(1):73–90. doi:10.1177/0142331214532002.
   Web of Science ®Google Scholar
• Chauhan SS, Bajpai D. 2012. Collision free autonomous robot path planning. Int J Eng Res Tech. 1(8):1–3. doi:10.17577/IJERTV1IS8182.
   Google Scholar
• Fang S, Ma J. 2020. Influences of urban tunnel length on visual characteristics of drivers. J Transp Inform Safety. 38(06):24–30. doi:10.3963/j.jssn.1674-4861.2020.06.004
   Google Scholar
• Huang H, Wang J, Fei C, Zheng X, Xu Q. 2020. A probabilistic risk assessment framework considering lane-changing behavior interaction. Sci China-Inform Sci. 63(9):190203. doi:10.1007/s11432-019-2983-0.
   Web of Science ®Google Scholar
• Khatib O. 1985. Real-time obstacle avoidance for manipulators and mobile robots. IEEE International Conference on Robotics and Automation, 2:500–505. doi:10.1007/978-1-4613-8997-2_29.
   Google Scholar
• Lee J, Kirytopoulos K, Pervez A, Huang H. 2022. Understanding drivers’ awareness, habits and intentions inside road tunnels for effective safety policies. Accid Anal Prev. 172:106690. doi:10.1016/j.aap.2022.106690.
   PubMed Web of Science ®Google Scholar
• Luo J, Li S, Li H, Xia F. 2022. Intelligent network vehicle driving risk field modeling and path planning for autonomous obstacle avoidance. Proc Inst Mech Eng, Part C: J Mech Eng Sci. 236(15):8621–8634. doi:10.1177/09544062221085886.
   Web of Science ®Google Scholar
• Mancini M, Bloise N, Capello E, Punta E. 2020. Sliding mode control techniques and artificial potential field for dynamic collision avoidance in rendezvous maneuvers. IEEE Control Syst Lett. 4(2):313–318. doi:10.48550/arXiv.1906.10945.
   Google Scholar
• Ni D. 2013. A unified perspective on traffic flow theory. Part I: The field theory. Appl Math Sci. 7:1929–1946. doi:10.1061/41186(421)420. 10.12988/ams.2013.13175
   Google Scholar
• Olfati-Saber R. 2006. Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Trans Autom Control. 51(3):401–420. doi:10.1109/TAC.2005.864190.
   Web of Science ®Google Scholar
• Pervez A, Huang H, Han C, Wang J, Li Y. 2020. Revisiting freeway single tunnel crash characteristics analysis: A six-zone analytic approach. Accid Anal Prev. 142:105542. doi:10.1016/j.aap.2020.105542.
   PubMed Web of Science ®Google Scholar
• Pervez A, Huang H, Lee J, Han C, Li Y, Zhai X. 2022. Factors affecting injury severity of crashes in freeway tunnel groups: A random parameter approach. J Transp Eng, Part A: Syst. 148(4):04022006. doi:10.1061/JTEPBS.0000617.
   Web of Science ®Google Scholar
• Shin Y, Kim E. 2021. Hybrid path planning using positioning risk and artificial potential fields. Aerospace Sci Technol. 112(6):106640. doi:10.1016/j.ast.2021.106640.
   Google Scholar
• Tao P, Jin S, Wang D. 2011. Artificial potential energy field based heel-chase model. J Southeast Univ (Natural Science Edition). 41(04):854–858. doi:10.3969/j.issn.1001-0505.2011.04.037.
   Google Scholar
• Tian Y, Pei H, Yang J, Hu J, Pei X. 2021. An improved model of driving risk field for connected and automated vehicles. IEEE International Intelligent Transportation Systems Conference, 285–291. doi:10.1109/itsc48978.2021.9565069.
   Google Scholar
• Tian Y, Pei HX, Zhang Y. 2020. Path planning for cavs considering dynamic obstacle avoidance based on improved driving risk field and A* algorithm. 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), 281–286. doi:10.1109/ISCTT51595.2020.00056.
   Google Scholar
• Wang J, Pervez A, Wang Z, Han C, Hu L, Huang H. 2018. Crash analysis of Chinese freeway tunnel groups using a five-zone analytic approach. Tunnelling Underground Space Technol. 82:358–365. doi:10.1016/j.tust.2018.08.037.
   Web of Science ®Google Scholar
• Wang J, Wu J, Li Y. 2015a. The driving safety field based on driver–vehicle–road interactions. IEEE Trans Intell Transp Syst. 16(4):2203–2214. doi:10.1109/TITS.2015.2401837.
   Web of Science ®Google Scholar
• Wang J, Wu J, Li Y. 2016. Concept, principle and modeling of driving risk field based on human vehicle road cooperation. Chin J Highways. 29(01):105–114. doi:10.3969/j.issn.1001-7372.2016.01.014.
   Google Scholar
• Wang JQ, Wu J, Li Y, Li KQ. 2014. The concept and modeling of driving safety field based on driver-vehicle-road interactions. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 974–981. doi:10.1109/ITSC.2014.6957815.
   Google Scholar
• Wang JQ, Zheng Y, Li XF, Yu CF, Kodaka K, Li KQ. 2015b. Driving risk assessment using near-crash database through data mining of tree-based model. Accid Anal Prev. 84:54–64. doi:10.1016/j.aap.2015.07.007.
   PubMed Web of Science ®Google Scholar
• Wolf MT, Burdick JW. 2008. Artificial potential functions for highway driving with collision avoidance. 2008 IEEE International Conference on Robotics and Automation, 3731–3736. doi:10.1109/ROBOT.2008.4543783.
   Google Scholar
• Yang X. 2021. Modeling of vehicle driving behavior in tunnel entrance zone based on artificial potential field theory. 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), 678–683. doi:10.1109/IPEC51340.2021.9421333.
   Google Scholar
• Yang Z, Yu Y, Yu D, Zhou H, Mo X. 2013. Apf-based car following behavior considering lateral distance. Adv Mech Eng. 5:1255–1260. doi:10.1155/2013/207104.
   Web of Science ®Google Scholar
• Zhou Z, Ma J, Lu T, Li G, Fang S, Tan T. 2020. An evaluation method for visual search stability in urban tunnel entrance and exit sections based on Markov chain. IEEE Access. 8:68559–68569. doi:10.1109/ACCESS.2020.2986272.
   Web of Science ®Google Scholar